Multi-system, multi-parameter, integrated, comprehensive early warning method and system for coal and rock dynamic disaster

ABSTRACT

A multi-system, multi-parameter, integrated, comprehensive early warning method for a coal and rock dynamic disaster includes: obtaining monitoring data of a plurality of monitoring systems for the coal rock dynamic disaster, and extracting multivariate characteristic parameters capable of reflecting precursor information of the coal and rock dynamic disaster in each monitoring system based on the monitoring data; screening out a combination of characteristic parameters with a highest early warning effectiveness in each monitoring system and an optimal critical value of each characteristic parameter based on the multivariate characteristic parameters; calculating a comprehensive early warning index and an early warning effectiveness of each monitoring system; and calculating a multi-system comprehensive early warning result of the coal and rock dynamic disaster based on the comprehensive early warning index and the early warning effectiveness of each monitoring system.

CROSS REFERENCE TO THE RELATED APPLICATIONS

This application is based upon and claims priority to Chinese PatentApplication No. 201911047938.8, filed on Oct. 30, 2019, the entirecontents of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to the field of early warning technologyof coal and rock dynamic disaster, and more particularly, to amulti-system, multi-parameter, integrated, comprehensive, early warningmethod and system for a coal and rock dynamic disaster.

BACKGROUND

Coal and rock dynamic disasters are a common type of disaster in coalmines and are very destructive. In a coal and rock dynamic disaster, themost undesirable result is shaft and roadway closure with large volumesof coal and rock mass ejected. Such conditions result not only inenvironmental and economic loss but also loss to person and property.Most mines where operations have been long-term include deep mining aspart of their operations. Deep mining presents an increase in thefrequency and intensity of coal and rock dynamic disasters. The reasonsfor coal and rock dynamic disasters are complex and dynamic, whichbrings challenges to monitoring, early warning, prevention and control.An accurate and efficient monitoring and early warning method has,therefore, become a major focus for identifying disaster risks in coalmines in advance, taking control measures, and ensuring safe production.

At present, real-time monitoring methods for coal and rock dynamicdisasters in mines typically include micro-seismic monitoring, earthsound monitoring, support stress monitoring and electromagneticradiation monitoring. Each of the current disaster monitoring andforecasting methods have limitations. Micro-seismic monitoring is mainlyaimed at low-frequency/high-energy events, and is employed inlarge-scale area monitoring to realize the temporal-order monitoring ofhigh-scale coal and rock fractures as well as the spatial location ofrupture sources. Earth sound monitoring is mainly aimed athigh-frequency/low-energy events, and can realize the local monitoringof low-scale coal and rock micro-fractures and the monitoring range ofthe sensors typically used is about 30 m. Support stress monitoring islimited to monitoring only the ceiling weight of the stope support.Electromagnetic radiation monitoring can monitor the stressconcentration and the fracture of the interior of coal and rock massconcurrently, and the monitoring range can reach 20 m. Due to differentmonitoring methods and limitations of the equipment associated withthem, however, early warning results of multi-source monitoring dataobtained from heretofore monitoring methods and equipment may beinconsistent, which results in inefficient use of instruments andequipment and inaccuracies. False alarms are a common problem. There is,therefore, a need for further analyzing the data generated from currentmonitoring systems to improve data accuracy. Such data analytics shouldalso improve the accuracy of early warnings and the safety ofunderground production.

SUMMARY

In order to overcome existing disadvantages in the above-mentioned priorart, the present invention provides a multi-system, multi-parameter,integrated, comprehensive early warning method and system for a coal androck dynamic disaster. The method and system of the invention analyzesthe monitoring data of each monitoring system, organically integratesthe monitoring and early warning information obtained, and constructs amulti-system, multi-parameter, integrated, comprehensive monitoring andearly warning model, thereby forming an integrated, comprehensive,highly reliable early warning method with a unified early warningcriteria, a unified early warning indicator and a unified early warningcritical value. In this way, the early warning accuracy of coal and rockdynamic disasters is improved, the frequency of false alarms is reduced,thereby substantially contributing to the prevention and control of minedisasters.

To solve the above technical problems, the present invention provides amulti-system, multi-parameter, integrated, comprehensive early warningmethod for a coal and rock dynamic disaster, wherein the multi-system,multi-parameter, integrated, comprehensive early warning method for thecoal and rock dynamic disaster includes:

obtaining monitoring data from a plurality of monitoring systems for thecoal rock dynamic disaster, and extracting multivariate characteristicparameters capable of reflecting precursor information of the coal androck dynamic disaster in each monitoring system based on the monitoringdata;

screening out a combination of characteristic parameters with a highestearly warning effectiveness in each monitoring system and an optimalcritical value of each characteristic parameter based on themultivariate characteristic parameters;

calculating a comprehensive early warning index and an early warningeffectiveness of each monitoring system based on the combination of thecharacteristic parameters with the highest early warning effectivenessin each monitoring system and the optimal critical value of eachcharacteristic parameter; and

calculating a multi-system comprehensive early warning result of thecoal and rock dynamic disaster based on the comprehensive early warningindex and the early warning effectiveness of each monitoring system.

Further, after calculating the multi-system comprehensive early warningresult of the coal and rock dynamic disaster, the method furtherincludes:

comparing the multi-system comprehensive early warning result of thecoal and rock dynamic disaster with a classification early warningevaluation criteria of the coal and rock dynamic disasters to determinea risk level of the dynamic disaster.

As preferred, the monitoring systems include on-line monitoring systemsand portable monitoring systems, wherein the on-line monitoring systemsinclude a micro-seismic monitoring system, an earth sound monitoringsystem and a hydraulic support monitoring system.

As preferred, the monitoring data include continuous monitoring datacollected by the on-line monitoring systems and discrete monitoring datacollected by the portable monitoring systems.

As preferred, dimensions of the multivariate characteristic parametersinclude a time dimension, a space dimension and an intensity dimension,the multivariate characteristic parameters include:

in the micro-seismic monitoring system, a frequency, a frequency ratioand a frequency deviation which reflect temporal order information;dispersion which reflects spatial information; micro-seismic energy, amicro-seismic energy deviation and dispersion which reflect intensityinformation;

in the earth sound monitoring system, earth sound energy, an earth soundenergy deviation, an earth sound energy average value and a pulsefactor, which reflect the intensity information; a pulse which reflectsthe temporal order information; and

in the hydraulic support monitoring system, a flicker interval riskdegree which reflects the temporal order information; a recordingfrequency and support pressure which reflect the intensity information.

Further, the step of screening out the combination of the characteristicparameters with the highest early warning effectiveness in eachmonitoring system and the optimal critical value of each characteristicparameter includes:

performing a pairwise combination on characteristic parameters belongingto different dimensions in the same monitoring system;

adopting a genetic algorithm to train and select a combination ofcharacteristic parameters with a highest fitness value in eachmonitoring system, and calculating an optimal critical value of eachcharacteristic parameter;

taking the combination of the characteristic parameters with the highestfitness value in each monitoring system as the combination of thecharacteristic parameters with the highest early warning effectivenesscorresponding to each monitoring system.

Further, when the genetic algorithm is adopted to train and select thecombination of the characteristic parameters with the highest fitnessvalue in each monitoring system, the first two groups of thecombinations of the characteristic parameters with the highest fitnessvalue are selected in the micro-seismic monitoring system; and one groupof the combination of the characteristic parameters with the highestfitness value corresponding to each sensor is selected in the earthsound monitoring system and the hydraulic support monitoring system.

Further, the step of calculating the comprehensive early warning indexand the early warning effectiveness of each monitoring system based onthe combination of the characteristic parameters with the highest earlywarning effectiveness in each monitoring system and the optimal criticalvalue of each characteristic parameter includes:

based on the combination of the characteristic parameters with thehighest fitness value in each monitoring system and the optimal criticalvalue of each characteristic parameter, calculating a single systemearly warning degree of each monitoring system according to thefollowing formula:

$W_{i} = \left\{ \begin{matrix}{0,} & {a_{i} < V_{1}} & \& & {b_{i} < V_{2}} \\{1,} & {a_{i} < V_{1}} & {or} & {b_{i} < V_{2}} \\{2,} & {a_{i} > V_{1}} & \& & {b_{i} > V_{2}}\end{matrix} \right.$

wherein, for the micro-seismic monitoring system, the single systemearly warning degree is an early warning degree of each group of thecombination of the characteristic parameters with the highest fitnessvalue, wherein, W_(i) represents an early warning degree of an i^(th)group of the combination of the characteristic parameters with thehighest fitness value, a_(i) and b_(i) represent real-time values of twocharacteristic parameters in the i^(th) group of the combination of thecharacteristic parameters with the highest fitness value, respectively,and V₁ and V₂ represent the optimal critical values corresponding toeach characteristic parameter in the i^(th) group of the combination ofthe characteristic parameters with the highest fitness value,respectively;

for the earth sound monitoring system and the hydraulic supportmonitoring system, the single system early warning degree is an earlywarning degree of each sensor, wherein, W_(i) represents an earlywarning degree of an i^(th) sensor, a_(i) and b_(i) represent thereal-time values of two characteristic parameters in the combination ofthe characteristic parameters with the highest fitness valuecorresponding to the i^(th) sensor, respectively, and V₁ and V₂represent the optimal critical values corresponding to eachcharacteristic parameter in the combination of the characteristicparameters with the highest fitness value corresponding to the i^(th)sensor, respectively;

based on the single system early warning degree of each monitoringsystem, calculating a comprehensive early warning index W_(C) of eachmonitoring system according to the following formula:

$W_{C} = {\sum\left( {\frac{W_{i}}{\max\left( W_{i} \right)} \times \frac{R_{i}}{\sum R_{i}}} \right)}$

wherein, for the micro-seismic monitoring system, R_(i) represents afitness value of the i^(th) group of the combination of thecharacteristic parameters with the highest fitness value; for the earthsound monitoring system and the hydraulic support monitoring system,R_(i) represents a fitness value of the combination of thecharacteristic parameters with the highest fitness value correspondingto the i^(th) sensor;

based on the comprehensive early warning index of each monitoringsystem, calculating an early warning effectiveness R_(I) of eachmonitoring system according to the following formula, wherein R_(I)represents an early warning effectiveness of the I^(th) monitoringsystem:

$R_{I} = {\frac{n_{1}^{1}}{N_{1}} - \frac{t_{0}}{T_{0}}}$

wherein, n₁ ¹ represents a number of times of early warnings generatedand alarmed rightly in a monitoring time; N₁ represents a total numberof times of events with large energy or shock in the monitoring time; t₀is time taken to generate an early warning; T₀ is total monitoring time;when the comprehensive early warning index corresponding to themonitoring system exceeds a preset threshold within preset days beforethe event with large energy or shock occurs, the early warming is right,otherwise the early warming is false

Further, the step of calculating the multi-system comprehensive earlywarning result of the coal and rock dynamic disaster based on thecomprehensive early warning index and the early warning effectiveness ofeach monitoring system includes:

based on the comprehensive early warning index and the early warningeffectiveness of each monitoring system, calculating the multi-systemcomprehensive early warning result I_(C) of the coal and rock dynamicdisaster according to the following formula:

$I_{C} = {\sum\left( {\frac{W_{CI}}{\max\left( W_{CI} \right)} \times \frac{R_{I}}{\sum R_{I}}} \right)}$

wherein, W_(CI) represents a comprehensive early warning index of theI^(th) monitoring system, and R_(I) represents an early warningeffectiveness of the I^(th) monitoring system.

In order to solve the above technical problems, the present inventionprovides a multi-system multi-parameter integrated comprehensive earlywarning system for a coal and rock dynamic disaster, wherein themulti-system multi-parameter integrated comprehensive early warningsystem for the coal and rock dynamic disaster includes:

a multivariate characteristic parameter extracting module, configured toobtain monitoring data of a plurality of monitoring systems for the coalrock dynamic disaster, and extract multivariate characteristicparameters capable of reflecting precursor information of the coal androck dynamic disaster in each monitoring system based on the monitoringdata;

a characteristic parameter combination screening module, configured toscreen out a combination of characteristic parameters with a highestearly warning effectiveness in each monitoring system and an optimalcritical value of each characteristic parameter based on themultivariate characteristic parameters;

a single system early warning index and early warning effectivenesscalculation module, configured to calculate a comprehensive earlywarning index and an early warning effectiveness of each monitoringsystem based on the combination of the characteristic parameters withthe highest early warning effectiveness in each monitoring system andthe optimal critical value of each characteristic parameter; and

a comprehensive early warning result calculation module, configured tocalculate a multi-system comprehensive early warning result of the coaland rock dynamic disaster based on the comprehensive early warning indexand the early warning effectiveness of each monitoring system.

The advantages of the above technical solution of the present inventionare as follows.

The monitoring data obtained the device in a micro-seismic monitoringsystem, an earth sound monitoring system, a support stress monitoringsystem and other monitoring system are comprehensively evaluated andanalyzed to obtain the early warning characteristic parameters of eachsystem; a pairwise combination of characteristic parameters in eachmonitoring system is input into a genetic algorithm to obtain theoptimal critical value V₁, V₂ and the corresponding fitness value of thecombination of the characteristic parameters, and a combination of thecharacteristic parameters with the highest fitness value is selected asthe combination of the characteristic parameters corresponding to eachmonitoring system; an early warning degree W_(i) and a comprehensiveearly warning index W_(C) of each single system are calculated, and anearly warning effectiveness RI of each single system is obtained by theR-value evaluation method; finally, a multi-system comprehensive earlywarning result of the coal and rock dynamic disaster is calculated todetermine the current risk degree of the coal and rock dynamic disasterand the measures taken. The method combines advantages of the pluralityof monitoring systems, improves the accuracy of early warning andremoves the influence of subjective factors, which can play a guidingrole in the prevention and control of mine disasters.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a temporal order variation curve of the dispersion.

FIG. 2 shows a temporal order variation curve of the frequency ratio.

FIG. 3 shows a temporal order variation curve of the energy deviation.

FIG. 4 shows a temporal order variation curve of the frequencydeviation.

FIG. 5 shows a temporal order variation curve of the energy averagevalue (logarithm).

FIG. 6 shows a temporal order variation curve of the pulse factor.

FIG. 7 shows a temporal order variation curve of the recordingfrequency.

FIG. 8 shows a temporal order variation curve of the flicker intervalrisk degree.

FIG. 9 shows the results of multi-system integrated early warning.

FIG. 10 is a flow chart of the multi-system multi-parameter integratedcomprehensive early warning method and system for coal and rock dynamicdisaster.

FIG. 11 is a flow chart of calculating an optimal critical value by thegenetic algorithm of the present invention.

FIG. 12 is an operation process diagram of the genetic algorithm adoptedby the present invention in the Matrix Laboratory (MATLAB) ver. 2014 b.

DETAILED DESCRIPTION OF THE EMBODIMENTS

In order to illustrate the technical problems to be solved, technicalsolutions and advantages of the present invention clearly, a detaileddescription is provided in combination with the drawings and specificembodiments.

First Embodiment

Referring to FIGS. 1 to 12, the present embodiment provides amulti-system, multi-parameter, integrated, comprehensive early warningmethod for a coal and rock dynamic disaster, as shown in FIG. 10(wherein, the symbol “ ” represents other continuous monitoring data ordiscrete monitoring data that can be read on-line in real time in eachmonitoring system). The method includes the following steps.

S101: obtaining monitoring data from a plurality of monitoring systemsfor the coal rock dynamic disaster, and extracting multivariatecharacteristic parameters capable of reflecting precursor information ofthe coal and rock dynamic disaster in each monitoring system based onthe monitoring data;

It should be noted that the above monitoring systems are the installedmonitoring systems in the mine, and include on-line monitoring systemsand portable monitoring systems. Specifically, the on-line monitoringsystems include, but are not limited to, the micro-seismic monitoringsystem, the earth sound monitoring system and the hydraulic supportmonitoring system. The above monitoring data include the continuousmonitoring data collected by the on-line monitoring systems and thediscrete monitoring data collected by the portable monitoring systems.Further, the monitoring data include not only the data that can be readdirectly, but also the deep multivariate characteristic parametersobtained after data mining, such as time, space and intensity.

The multivariate characteristic parameters are time, space andintensity, which include but are not limited to:

in the micro-seismic monitoring system, a frequency, a frequency ratioand a frequency deviation which reflect the temporal order information;dispersion which reflects the spatial information; micro-seismic energy,a micro-seismic energy deviation and dispersion which reflect theintensity information;

in the earth sound monitoring system, earth sound energy, an earth soundenergy deviation, an earth sound energy average value and a pulsefactor, which reflect the intensity information; a pulse which reflectsthe temporal order information; and

in the hydraulic support monitoring system, a flicker interval riskdegree which reflects the temporal order information; a recordingfrequency and support pressure which reflect the intensity information.

Specifically, the above multivariate characteristic parameters arecalculated as follows.

The value of the frequency ratio P (t) is calculated by the followingformula:

${P(t)} = \frac{a(t)}{b(T)}$

wherein, a(t) represents an average frequency in the n^(th) hour (e.g. 8hours) before the occurrence of the large energy event, and b(T)represents an average frequency in the n days (e.g. 2 days) before thelarge energy event.

The value of the frequency deviation D_(P)(t) is calculated by thefollowing formula:

${D_{P}(t)} = \frac{{N(t)} - \overset{\_}{N(T)}}{\overset{\_}{N(T)}}$

wherein, N(t) represents a number of micro-seismic events on that day,and N(T) represents an average daily frequency of the micro-seismicevents between two large-energy mine earthquake events (such as, energy>10⁶J).

The value of the dispersion Q(t) is calculated by the following formula:

${Q(t)} = {\lg\left( {\frac{T_{E}(t)}{L_{E}(t)} + 1} \right)}$

wherein, T_(E)(t) is a sum of maximum energy and median energy in aperiod of time; L_(E)(t) is micro-seismic critical characteristicenergy. The calculation steps for the dispersion are as follows:

(1) calculating a weighted average distance d of the locations of allmicro-seismic events occurring in a recent period of time (e.g. thefirst 8 hours):

$d = \sqrt{\frac{{\sum{w_{i}\left( {x_{i} - \overset{\_}{x}} \right)}^{2}} + {w_{i}\left( {y_{i} - \overset{\_}{y}} \right)}^{2} + {w_{i}\left( {z_{i} - \overset{\_}{z}} \right)}^{2}}{\sum w_{i}}}$

wherein, a total of n micro-seismic events occurred in the recent periodof time. The average values of x, y and z refer to the average values ofthe n micro-seismic events, and for an i^(th) micro-seismic event, theircoordinates are x_(i), y_(i) and z_(i). w_(i) is a root value of theenergy of the i^(th) micro-seismic event.

(2) ranking the calculated weighted average distance of the historicallocations of micro-seismic events from small to large;

(3) finding out the serial number of the weighted average distancerespectively corresponding to the locations a₁, a₂, a₃, a₄ . . . a_(x),and then rounding down to obtain the serial numbers corresponding to xweighted average distances (wherein x can be 7, and the correspondinga_(i) values can be 1.2%, 7.04%, 27.5%, 80.84%, 88.51%, 94.84%, 98.34%);

(4) arranging all the characteristic energy points within a certainrange (within one meter) of the left radius and right radius of eachweighted average distance obtained above in the order from small tolarge;

(5) extracting the characteristic energy points within a certain rangearound each weighted average distance, wherein the number of the rangeis the same as x in step (3), the corresponding range can take all thevalues in (40%-45%), (45%-50%), (98%-99%), (98%-99%), (98%-99%),(90%-98%) and (85%-90%); calculating the average value of the extractedcharacteristic energy points to obtain the critical characteristicenergy point, so as to obtain the critical characteristic energyL_(E)(t);

(6) finding out the weighted average distance corresponding to thecritical characteristic energy L_(E)(t), that is, finding out x pointsin the coordinate system corresponding to the weighted average distanceand the characteristic energy, and using the interpolation method to getthe critical characteristic energy curve;

(7) substituting the data into

${Q(t)} = {\lg\left( {\frac{T_{E}(t)}{L_{E}(t)} + 1} \right)}$

to obtain the dispersion.

The value of the micro-seismic energy deviation D_(E)(t) is calculatedby the following formula:

${D_{E}(t)} = \frac{{E(t)} - \overset{\_}{E(T)}}{\overset{\_}{E(T)}}$

wherein, E(t) represents energy of the micro-seismic event at a tmoment, and E(T) represents average energy of each micro-seismic eventbetween two large-energy mine earthquake events (such as, energy >10⁶J).

The value of the earth sound energy deviation is calculated by thefollowing formula:

${D(t)} = \frac{{P(t)} - \overset{\_}{P\left( {T(t)} \right)}}{\overset{\_}{P\left( {T(t)} \right)}}$

wherein,

${\overset{\_}{P\left( {T(t)} \right)} = \frac{\sum\limits_{1}^{n}{P\left( {T(t)} \right)}_{|{t \in {T{(t)}}}}}{n}},$

P(t) is a time window length obtained by dividing a cumulative sum ofthe parameters by a period of time (e.g. 10 minutes), T(t) represents atime interval which is related to the t moment, wherein T(t) is the timeinterval from a time (e.g. 24 hours) before the t moment to the tmoment, and n is a number of earth sound weighted energy in the timeinterval T(t).

The earth sound energy average value A_(E) is average energy within agiven time (e.g. 6 hours).

The value of the pulse factor P is calculated by the following formula:

$P = \frac{10{\sum\limits_{i = 1}^{n/10}E_{i}}}{\sum\limits_{i}^{n}\; E_{i}}$

wherein, n is a number of a certain time window (e.g. 10 min) in arecent period of time (e.g. 6 hours); the calculation method of

$10{\sum\limits_{i = 1}^{n/10}E_{i}}$

is as follows: the cumulative energy value of the earth sound in thecertain time window (e.g. 10 min) of the recent period of time (e.g. 6hours) is arranged from large to small, which is the average value ofthe first n/10 numbers (rounded down) and

$\sum\limits_{i = 1}^{n}E_{i}$

is the average value of the cumulative energy value of the earth soundin the certain time window (e.g. 10 min) of the recent period of time(e.g. 6 hours).

The recording frequency H is an acquisition frequency of the supportpressure in the recent period of time (e.g. 6 hours).

The calculation method of the flicker interval risk degree is asfollows:

${I = \frac{1}{t_{2} - t_{1}}},$

wherein, t₁ represents a time of a previous flicker and t₂ represents atime of a subsequent flicker; the flicker is recorded as follows; anaverage pressure value over a period of time (e.g. 2 days) is recordedas an average value, and if the subsequent pressure data decreases fromhigher than the average value to below a certain range (which can be1/10 of the average value) of the average value, and rises to above theaverage value again, then one flicker is recorded.

S102: screening out a combination of characteristic parameters with ahighest early warning effectiveness in each monitoring system and anoptimal critical value of each characteristic parameter based on themultivariate characteristic parameters.

It should be noted that, in the present embodiment, the above stepspecifically adopts a genetic algorithm to train and select thecombination of the characteristic parameters with the highest earlywarning effectiveness in each monitoring system and the correspondingoptimal critical value.

Specifically, the genetic algorithm is an algorithm that performs aguided process in the target space to search for the optimal solution,which imitates the mechanism of biological evolution in nature. Thegenetic algorithm has the inherent characteristics of simultaneousoptimization of multi-parameters and multi-combinations, and candirectly operate on structural objects, without the limitation ofderivatives and function continuity. The essence of the geneticalgorithm is to make use of the mechanism of “survival of the fittest”in nature, and finally screens out the samples with high fitness to theenvironment through iterative genetic operations such as selection,crossover and mutation. Furthermore, the genetic algorithm adopts theprobabilistic optimization method and uses the evaluation information ofthe fitness function instead of the objective function of thetraditional derivative, and there is no requirement for the objectivefunction in form thereof, so it has good fitness and large-scalecharacteristics.

Specifically, in the present embodiment, the process of adopting thegenetic algorithm to train and select the combination of thecharacteristic parameters with the highest early warning effectivenessin each monitoring system and the corresponding optimal critical valueincludes:

(1) performing a pairwise combination on characteristic parametersbelonging to different dimensions in the same monitoring system, thatis, performing a combination of the characteristic parameters belongingto three different dimensions of time, space and intensity in eachmonitoring system;

(2) adopting the genetic algorithm to train and select a combination ofthe characteristic parameters with the highest fitness value in eachmonitoring system, and calculating the optimal critical value of eachcharacteristic parameter;

(3) taking the combination of the characteristic parameters with thehighest fitness value in each monitoring system as the combination ofthe characteristic parameters with the highest early warningeffectiveness corresponding to each monitoring system.

Further, the above process of adopting the genetic algorithm to trainand select the combination of the characteristic parameters with thehighest fitness value in each monitoring system and calculating theoptimal critical value is shown in FIG. 11, including:

(i) inputting a combination of characteristic parameters of eachmonitoring system;

(ii) generating an initial sample according to the data input by step(i);

(iii) using the fitness function of the genetic algorithm to calculatethe fitness;

(iv) judging whether the termination condition set in the geneticalgorithm is satisfied or not according to the fitness calculated instep (iii);

(v) if the judgment of step (iv) satisfies the termination condition,then outputting the optimal critical value of the characteristicparameters; if the judgment of step (iv) does not satisfy thetermination condition, then proceeding to step (vi); and

(vi) re-obtaining the initial sample generated by performing theselection, crossover and mutation in turn in step (ii), retaining theindividual with the highest fitness value and at the same timeeliminating the individual with the low fitness value, so as to generatea new sample, and then returning to step (iii) for calculation until thetermination condition is satisfied.

Specifically, in the above step (iii), the fitness function of thegenetic algorithm adopts an R-value evaluation method. The R-valueevaluation method is a method proposed by Geophysics Institute in the1970s to evaluate the effectiveness of earthquake prediction, which isclear and easy to operate, and neither false alarm nor underreporting isencouraged, which have been widely used in earthquake prediction fordecades and achieved good results. The R-value evaluation method isshown as the following formula:

$R = {\frac{n_{1}^{1}}{N_{1}} - \frac{n_{0}^{1}}{N_{0}}}$

wherein, n₁ ¹ represents a number of times that early warnings aregenerated and alarmed rightly; N₁ represents a total number of times ofevents with large energy or shock; n₀ ¹ represents a number of timesthat early warnings are generated but alarmed falsely; N₀ represents atotal number of events without large energy or large shock, wherein theevent with large energy refers to a micro-seismic event with energygreater than 10⁵J and the event with shock refers to an event that ashock occurs. It is defined that if the value of characteristicparameter exceeds the optimal critical value within two days before theevent with shock or large energy, the early warming is right, otherwisethe early warming is false. The accuracy of the early warning isincreased as the R-value approaches 1. The combination of characteristicparameters a and b with the largest fitness value R and theircorresponding optimal critical values V₁ and V₂ are selected.

The running process of the genetic algorithm in the MATLAB ver. 2014b isshown in FIG. 12, wherein a is a penalty value (1-fitness value) of thefitness function in each iteration; b is a number of iterations.

In addition, when the genetic algorithm is adopted to train and selectthe combination of the characteristic parameters with the highestfitness value in each monitoring system, for the micro-seismicmonitoring system, since the micro-seismic monitoring system monitorsthe micro-seismic events in the whole mining area and all probescomprehensively collect a set of monitoring results, it is required toselect the first two groups of the combination of the characteristicparameters with the highest fitness value; and

for the earth sound monitoring system and the hydraulic supportmonitoring system, since they collect monitoring information indifferent ranges, respectively, and each sensor has a set of monitoringresults, one group of the combination of the characteristic parameterswith the highest fitness value corresponding to each sensor is selected.

S103: calculating a comprehensive early warning index and an earlywarning effectiveness of each monitoring system based on the combinationof the characteristic parameters with the highest early warningeffectiveness in each monitoring system and the optimal critical valueof each characteristic parameter.

It should be noted that in the present embodiment, the above stepspecifically includes:

(1) based on the combination of the characteristic parameters with thehighest fitness value in each monitoring system and the optimal criticalvalue of each characteristic parameter, calculating a single systemearly warning degree of each monitoring system according to thefollowing formula:

$W_{i} = \left\{ \begin{matrix}{0,} & {a_{i} < V_{1}} & \& & {b_{i} < V_{2}} \\{1,} & {a_{i} < V_{1}} & {or} & {b_{i} < V_{2}} \\{2,} & {a_{i} > V_{1}} & \& & {b_{1} > V_{2}}\end{matrix} \right.$

wherein, for the micro-seismic monitoring system, the single systemearly warning degree is an early warning degree of each group of thecombination of the characteristic parameters with the highest fitnessvalue, wherein, W_(i) represents an early warning degree of an i^(th)group of the combination of the characteristic parameters with thehighest fitness value, a_(i) and b_(i) represent the real-time values oftwo characteristic parameters in the i^(th) group of the combination ofthe characteristic parameters with the highest fitness value,respectively, and V₁ and V₂ represent the optimal critical valuescorresponding to each characteristic parameter in the i^(th) group ofthe combination of the characteristic parameters with the highestfitness value, respectively;

for the earth sound monitoring system and the hydraulic supportmonitoring system, the single system early warning degree is an earlywarning degree of each sensor, wherein, W_(i) represents an earlywarning degree of an i^(th) sensor, a_(i) and b_(i) represent thereal-time values of two characteristic parameters in the combination ofthe characteristic parameters with the highest fitness valuecorresponding to the i^(th) sensor, respectively, and V₁ and V₂represent the optimal critical values corresponding to eachcharacteristic parameter in the combination of the characteristicparameters with the highest fitness value corresponding to the i^(th)sensor, respectively;

(2) based on the single system early warning degree of each monitoringsystem, calculating a comprehensive early warning index W_(C) of eachmonitoring system according to the following formula:

$W_{C} = {\sum\left( {\frac{W_{i}}{\max\left( W_{i} \right)} \times \frac{R_{i}}{\sum R_{i}}} \right)}$

wherein, for the micro-seismic monitoring system, R_(i) represents afitness value of the i^(th) group of the combination of thecharacteristic parameters with the highest fitness value; for the earthsound monitoring system and the hydraulic support monitoring system,R_(i) represents a fitness value of the combination of thecharacteristic parameters with the highest fitness value correspondingto the i^(th) sensor;

(3) based on the comprehensive early warning index of each monitoringsystem, calculating an early warning effectiveness R_(I) of eachmonitoring system by the R-value evaluation method according to thefollowing formula, wherein R_(I) represents an early warningeffectiveness of an I^(th) monitoring system:

$R_{I} = {\frac{n_{1}^{1}}{N_{1}} - \frac{t_{0}}{T_{0}}}$

wherein, n₁ ¹ represents the number of times that early warnings aregenerated and alarmed rightly in a monitoring time; N₁ represents atotal number of times of events with large energy or shock in themonitoring time; t₀ is time taken to generate an early warning; T₀ istotal monitoring time; when the comprehensive early warning indexcorresponding to the monitoring system exceeds a preset threshold withinthe preset days before the event with large energy or shock occurs, theearly warming is right, otherwise, the early warming is false.

Specifically, the present embodiment defines that if the value of theW_(C) exceeds 0.5 within 2 days before the event with large energy orshock occurs, the early warming is right, otherwise, the early warmingis false; and the duration of each early warning is 2 days.

S104: calculating a multi-system comprehensive early warning result ofthe coal and rock dynamic disaster based on the comprehensive earlywarning index and the early warning effectiveness of each monitoringsystem.

It should be noted that in the present embodiment, the above stepspecifically includes:

calculating the multi-system comprehensive early warning result I_(C) ofthe coal and rock dynamic disaster according to the following formula:

$I_{C} = {\sum\left( {\frac{W_{CI}}{\max\left( W_{CI} \right)} \times \frac{R_{I}}{\sum R_{I}}} \right)}$

wherein, W_(CI) represents a comprehensive early warning index of theI^(th) monitoring system, and R_(I) represents an early warningeffectiveness of the I^(th) monitoring system.

Further, after S104, the method of the present embodiment furtherincludes:

S105, comparing the multi-system comprehensive early warning result ofthe coal and rock dynamic disaster with a classification early warningevaluation criteria of the coal and rock dynamic disasters to determinea risk level of the dynamic disaster.

It should be noted that in the present embodiment, the risk leveldetermined by the above step according to the comparison result is: when0≤I_(C)≤0.25, it is in a no-risk state; when 0.25<I_(C)≤0.5, it is in aweak risk state; when 0.5<I_(C)≤0.75, it is in a medium risk state; when0.75<I_(C)≤1, it is in a strong risk state.

According to the early warning result of the present invention,corresponding measures can be taken according to Table 1.

TABLE 1 risk state classification table of the coal and rock dynamicdisasters Risk Risk Level W_(C) State Measures Taken I-level 0 ≤ I_(C) ≤0.25 No risk Carrying out design and production state operationsnormally under the mine. II-level 0.25 ≤ I_(C) ≤ Weak 1. Arrangingnecessary monitoring, 0.5 risk inspection and control equipment; state2. Making the monitoring and control plan, and carrying out the riskmonitoring, risk removing and effect test of rock burst during theoperation. III-level 0.5 ≤ I_(C) ≤ Medium 1. Arranging complete regional0.75 risk and local monitoring and state inspection equipment andcontrol equipment; 2. Carrying out a pre-pressure relief measure on thesupport pressure affected area and rock mass side of the coal miningface before state operation. 3. Regulating personnel restriction areasand determining disaster escape routes 4. Making the monitoring andcontrol plan, and carrying out the risk monitoring, risk removing andeffect test of rock burst during the operation. IV-level 0.75 ≤ I_(C) ≤1 Strong 1. Arranging complete regional risk and local monitoring andstate inspection equipment and control equipment; 2. Carrying out apre-pressure relief measure on the coal seam, the head-on side, and therear side of the mining roadway in the coal mining face, and thencarrying out the operation only after the risk of rock burst is removed;3. making the monitoring and control plan, and strengthening the riskmonitoring, risk removing and effect test of rock burst during theoperation; monitoring the disturbance influence on surrounding roadwayand chamber, and making corresponding control measures; 4. Setting upescape chambers, personnel restricted areas, and determining disasterescape routes.

In conjunction with the specific application scenario, the method of thepresent embodiment is further described below.

A certain working face of a certain mine is selected, and themicro-seismic monitoring system, the earth sound monitoring system andthe hydraulic support monitoring system are arranged near the workingface, respectively. The early warning steps are as follows.

(1) The historical monitoring data of each monitoring system of the coaland rock dynamic disaster in the mine are collected and analyzed, so asto extract the multivariate characteristic parameters (time, space andintensity) capable of reflecting the precursor information of the coaland rock dynamic disaster in each monitoring system, which includes thedispersion, the frequency ratio, the energy deviation and the frequencydeviation in the micro-seismic monitoring system; the energy averagevalue and the pulse factor in the earth sound monitoring system; and theflicker interval risk degree and the recording frequency in thehydraulic support monitoring system. Specifically, the dispersion is thecharacteristic parameter based on the spatial dimension. The possibilityof causing the shock event later increases as the dispersion of thesource position of the micro-seismic event in a period of timedecreases. The frequency ratio and the frequency deviation are thecharacteristic parameters of the time dimension. The occurrencefrequency of micro-seismic events before the shock event is lower thanthat before, and the phenomenon of “quiet period” appears, which leadsto the accumulation of energy for greater damage in the later stage. Theenergy average value and the energy deviation are the characteristicparameters of the intensity dimension. The internal fracture phenomenonof the coal and rock mass is aggravated as the energy value detected bythe micro-seismic monitoring system and the earth sound monitoringsystem increases. The pulse factor is the characteristic parameter ofthe time dimension, which indicates the relative peak value of the earthsound energy. The flicker interval risk degree is the characteristicparameter of the time dimension. The rapid change in the supportpressure is referred to as the flicker. The flicker can eliminate theinfluence caused by moving the support. The energy accumulation in coaland rock mass is aggravated as the occurrence frequency of the flickerincreases. The recording frequency is the characteristic parameter ofthe strength dimension. The recording frequency of the hydraulic supportsensor will increase when the stress in the coal and rock mass changes.The temporal order variation curves of the characteristic parameters aredrawn with two months as a time window, and the results are shown inFIGS. 1-8.

The pairwise combinations of the characteristic parameters of differentdimensions of each system are input into the genetic algorithm tocalculate the optimal critical values V₁ and V₂ and the correspondingfitness values R, and the two groups of the combination of thecharacteristic parameters with the highest fitness value of themicro-seismic monitoring system are selected, which are the combinationof the dispersion and the frequency ratio, the combination of the energydeviation and the frequency deviation; the combination of the energyaverage value (logarithm) and the pulse factor in the earth soundmonitoring system, and the combination of the recording frequency andthe flicker interval risk degree in the hydraulic support monitoringsystem, respectively. The calculation results are shown in Table 2.

TABLE 2 Calculation results of the genetic algorithm Optimal combinationof Monitoring characteristic parameters Fitness system name dispersionfrequency ratio value Micro-seismic short term 0.060 1.748 0.590monitoring long term 0.469 1.895 0.457 system energy average value(logarithm) pulse factor Earth sound 1#sensor 2.600 2.370 0.650monitoring 2#sensor 2.040 1.600 0.333 system 3#sensor 1.790 5.560 0.540recording flicker interval frequency risk degree Support 1#support 110.00078 0.143 monitoring 2#support 4 0.00001 0.500 system 3#support 90.00001 0.385

(3) The early warning degree W_(i) and the comprehensive early warningindex W_(C) of each single system are calculated, and the R-valueevaluation method is adopted to obtain the early warning effectivenessRI of each single system. During the historical monitoring period, atotal of 9 events with shock or high energy occurred in the workingface, which is numbered as {circle around (1)}-{circle around (9)} forconvenience of expression. The success or failure of the early warningis defined as follows: if the comprehensive early warning index W_(C)exceeds 0.5 within 3 days before the event with shock or high energyoccurs, the early warning is successful, otherwise, the early warningfails. The calculation results are shown in Table 3 below.

TABLE 3 Comprehensive early warning index W_(C) and early warningeffectiveness RI of a single system Comprehensive early warning indexW_(C) of events with Early shock or high energy corresponding to eachmonitoring warning Monitoring system effectiveness system name {circlearound (1)} {circle around (2)} {circle around (3)} {circle around (4)}{circle around (5)} {circle around (6)} {circle around (7)} {circlearound (8)} {circle around (9)} RI Micro-seismic 1.00 0.78 1.00 1.001.00 1.00 1.00 1.00 0.72 0.49 monitoring system Earth sound 1.00 1.001.00 0.53 0.66 0.87 0.87 0.80 0.72 0.77 monitoring system Support 0.600.81 0.00 1.00 1.00 1.00 1.00 1.00 1.00 0.73 monitoring system

Before the event with shock or large energy occurs, the comprehensiveearly warning index W_(C) of each system basically reaches the earlywarning standard, and the early warning result is accurate and has acertain reference value.

(4) According to the calculation results of Table 3 in step (3), theresult of the multi-system comprehensive early warning result of thecoal and rock dynamic disaster in the mine is calculated. The resultsare drawn with two months as the time window and shown in FIG. 7.

(5) The multi-system comprehensive early warning result I_(C) iscompared with the classification early warning evaluation criteria ofthe coal and rock dynamic disasters to determine the risk level: when0≤I_(C)≤0.25, it is in the no-risk state; when 0.25<I_(C)≤0.5, it is inthe weak risk state; when 0.5<I_(C)≤0.75, it is in the medium riskstate; when 0.75<I_(C)≤1, it is in the strong risk state.

The method of the present embodiment uses the result of the multi-systemcomprehensive early warning as the early warning index to carry out therisk early warning of the coal and rock dynamic disasters in a largearea of monitoring, and realizes the multi-system multi-parameterintegrated spatio-temporal comprehensive early warning. According to themethod, the accuracy is high, the operation is simple, and theprogramming operation can be realized, which lays a foundation for theefficient early warning of the coal and rock dynamic disasters.

Second Embodiment

The embodiment provides a multi-system multi-parameter integratedcomprehensive early warning system for a coal and rock dynamic disaster,wherein the multi-system, multi-parameter, integrated, comprehensiveearly warning system for the coal and rock dynamic disaster includes:

a multivariate characteristic parameter extracting module, configured toobtain monitoring data of a plurality of monitoring systems for the coalrock dynamic disaster, and extract multivariate characteristicparameters capable of reflecting precursor information of the coal androck dynamic disaster in each monitoring system based on the monitoringdata;

a characteristic parameter combination screening module, configured toscreen out a combination of characteristic parameters with a highestearly warning effectiveness in each monitoring system and an optimalcritical value of each characteristic parameter based on themultivariate characteristic parameters;

a single system early warning index and early warning effectivenesscalculation module, configured to calculate a comprehensive earlywarning index and an early warning effectiveness of each monitoringsystem based on the combination of the characteristic parameters withthe highest early warning effectiveness in each monitoring system andthe optimal critical value of each characteristic parameter; and

a comprehensive early warning result calculation module, configured tocalculate a multi-system comprehensive early warning result of the coaland rock dynamic disaster based on the comprehensive early warning indexand the early warning effectiveness of each monitoring system.

Further, in the present embodiment, the multi-system multi-parameterintegrated comprehensive early warning system for the coal and rockdynamic disaster further includes: a dynamic disaster risk leveldetermination module, configured to compare the multi-systemcomprehensive early warning result of the coal and rock dynamic disasterwith a classification early warning evaluation criteria of the coal androck dynamic disasters to determine a risk level of the dynamicdisaster.

The multi-system multi-parameter integrated comprehensive early warningsystem for the coal and rock dynamic disaster of the present embodimentcorresponds to the multi-system multi-parameter integrated comprehensiveearly warning method for the coal and rock dynamic disaster of the abovefirst embodiment. Specifically, the role of each module in themulti-system multi-parameter integrated comprehensive early warningsystem for the coal and rock dynamic disaster corresponds to the stepsof the multi-system multi-parameter integrated comprehensive earlywarning method for the coal and rock dynamic disaster, therefore notrepeat them here.

In addition, it should be noted that those skilled in the art shouldunderstand that the embodiments of the present invention may be providedas methods, devices, or computer program products. Therefore, theembodiments of the present invention may take the form of completehardware, complete software, or a combination of software and hardware.Furthermore, the embodiments of the present invention may take the formof a computer program product implemented on one or more computeravailable storage media (including, but not limited to, a disk memory, acompact disc read-only memory (CD-ROM), an optical memory, etc.)containing computer available program codes.

The embodiments of the present invention are described with reference toa method, a terminal device (system), and a flow chart and/or blockdiagram of a computer program product according to the embodiments ofthe present invention. It should be understood that each flow and/orblock in the flow chart and/or block diagram, as well as the combinationof the flow and/or block in the flow chart and/or block diagram, can beimplemented by computer program instructions. These computer programinstructions may be input into a processor of a general purposecomputer, embedded processor or other programmable data processingterminal device to generate a machine, such that instructions executedby the processor of the computer or other programmable data processingterminal device generate a device for performing a function specified inone flow or a plurality of flows in a flow chart, or one block or aplurality of blocks in a block diagram.

These computer program instructions can also be stored in acomputer-readable memory capable of guiding a computer or otherprogrammable data processing terminal device to work in a specificmanner, so that the instructions stored in the computer-readable memorygenerate a manufactured good including an instruction device, and theinstruction device performs the function specified in one flow or aplurality of flows in a flow chart, or one block or a plurality ofblocks in a block diagram. These computer program instructions can alsobe loaded on a computer or other programmable data processing terminaldevice so that a series of operating steps are performed on the computeror other programmable terminal devices to produce the computer-realizedprocessing. Therefore, the instructions executed on a computer or otherprogrammable terminal devices provide steps for implementing thefunction specified in one flow or a plurality of flows in a flow chart,or one block or a plurality of blocks in a block diagram.

It also should be noted that in the present application, the term“include”, “contain”, or any other similar terms are intended to covernon-exclusive inclusion, so that a process, method, article, or terminaldevice that includes a series of elements includes not only thoseelements, but also other elements that are not explicitly listed, orelements inherent in such the process, method, article, or terminaldevice. Without more restrictions, the elements defined by theexpression “include(s) a . . . ” do not exclude that there are otheridentical elements in the process, method, article or terminal deviceincluding the elements.

The above description is only preferred embodiments of the presentinvention. It should be noted that, for those skilled in the art, oncethe basic inventive concept of the present invention is known, withoutdeparting from the principles described in the present invention,several changes and modifications can be made to these embodiments ofthe present invention, and these changes and modifications shall withinthe scope of protection of the present invention. Therefore, theappended claims are intended to be construed as including the preferredembodiments and all changes and modifications that fall within the scopeof the embodiments of the invention.

1. A multi-system, multi-parameter, integrated, comprehensive earlywarning method for a coal and rock dynamic disaster, comprising:obtaining monitoring data from a plurality of monitoring systems for thecoal rock dynamic disaster, and extracting multivariate characteristicparameters in each monitoring system based on the monitoring data,wherein the multivariate characteristic parameters reflect precursorinformation of the coal and rock dynamic disaster; screening out acombination of characteristic parameters with a highest early warningeffectiveness in each monitoring system and an optimal critical value ofeach characteristic parameter based on the multivariate characteristicparameters; calculating a comprehensive early warning index and an earlywarning effectiveness of each monitoring system based on the combinationof the characteristic parameters with the highest early warningeffectiveness in each monitoring system and the optimal critical valueof each characteristic parameter; and calculating a multi-systemcomprehensive early warning result of the coal and rock dynamic disasterbased on the comprehensive early warning index and the early warningeffectiveness of each monitoring system.
 2. The multi-system,multi-parameter, integrated, comprehensive early warning method for thecoal and rock dynamic disaster according to claim 1, wherein aftercalculating the multi-system comprehensive early warning result of thecoal and rock dynamic disaster, the method further comprises: comparingthe multi-system comprehensive early warning result of the coal and rockdynamic disaster with a classification early warning evaluation criteriaof coal and rock dynamic disasters to determine a risk level of the coaland rock dynamic disaster.
 3. The multi-system, multi-parameter,integrated, comprehensive early warning method for the coal and rockdynamic disaster according to claim 1, wherein the plurality ofmonitoring systems comprise on-line monitoring systems and portablemonitoring systems, the on-line monitoring systems comprise amicro-seismic monitoring system, an earth sound monitoring system and ahydraulic support monitoring system.
 4. The multi-system,multi-parameter, integrated, comprehensive early warning method for thecoal and rock dynamic disaster according to claim 3, wherein themonitoring data comprise continuous monitoring data collected by theon-line monitoring systems and discrete monitoring data collected by theportable monitoring systems.
 5. The multi-system, multi-parameter,integrated, comprehensive early warning method for the coal and rockdynamic disaster according to claim 4, wherein dimensions of themultivariate characteristic parameters comprise a time dimension, aspace dimension and an intensity dimension; the multivariatecharacteristic parameters in the micro-seismic monitoring systemcomprise: a frequency, a frequency ratio and a frequency deviation,wherein the frequency, the frequency ratio and the frequency deviationreflect temporal order information; a first dispersion, wherein thefirst dispersion reflects spatial information; and micro-seismic energy,a micro-seismic energy deviation and a second dispersion, wherein themicro-seismic energy, the micro-seismic energy deviation and the seconddispersion reflect intensity information; the multivariatecharacteristic parameters in the earth sound monitoring system comprise:earth sound energy, an earth sound energy deviation, an earth soundenergy average value and a pulse factor, wherein the earth sound energy,the earth sound energy deviation, the earth sound energy average valueand the pulse factor reflect the intensity information; and a pulse,wherein the pulse reflects the temporal order information; and themultivariate characteristic parameters in the hydraulic supportmonitoring system comprise: a flicker interval risk degree, wherein theflicker interval risk degree reflects the temporal order information;and a recording frequency and support pressure, wherein the recordingfrequency and the support pressure reflect the intensity information. 6.The multi-system, multi-parameter, integrated, comprehensive earlywarning method for the coal and rock dynamic disaster according to claim5, wherein the step of screening out the combination of thecharacteristic parameters with the highest early warning effectivenessin each monitoring system and the optimal critical value of eachcharacteristic parameter comprises: performing a pairwise combination ontwo of characteristic parameters belonging to different dimensions in asame monitoring system; adopting a genetic algorithm to train and selecta combination of characteristic parameters with a highest fitness valuein each monitoring system, and calculating the optimal critical value ofeach characteristic parameter of the characteristic parameters with thehighest fitness value; and taking the combination of the characteristicparameters with the highest fitness value in each monitoring system asthe combination of the characteristic parameters with the highest earlywarning effectiveness corresponding to each monitoring system.
 7. Themulti-system, multi-parameter, integrated, comprehensive early warningmethod for the coal and rock dynamic disaster according to claim 6,wherein when the genetic algorithm is adopted to train and select thecombination of the characteristic parameters with the highest fitnessvalue in each monitoring system, first two groups of the combination ofthe characteristic parameters with the highest fitness value areselected in the micro-seismic monitoring system; and one group of thecombination of the characteristic parameters with the highest fitnessvalue corresponding to each sensor is selected in the earth soundmonitoring system and the hydraulic support monitoring system.
 8. Themulti-system, multi-parameter, integrated, comprehensive early warningmethod for the coal and rock dynamic disaster according to claim 7,wherein the step of calculating the comprehensive early warning indexand the early warning effectiveness of each monitoring system based onthe combination of the characteristic parameters with the highest earlywarning effectiveness in each monitoring system and the optimal criticalvalue of each characteristic parameter comprises: based on thecombination of the characteristic parameters with the highest fitnessvalue in each monitoring system and the optimal critical value of eachcharacteristic parameter, calculating a single system early warningdegree of each monitoring system according to the following formula:$W_{i} = \left\{ \begin{matrix}{0,} & {a_{i} < V_{1}} & \& & {b_{i} < V_{2}} \\{1,} & {a_{i} < V_{1}} & {or} & {b_{i} < V_{2}} \\{2,} & {a_{i} > V_{1}} & \& & {b_{1} > V_{2}}\end{matrix} \right.$ in the micro-seismic monitoring system, the singlesystem early warning degree is an early warning degree of each group ofthe combination of the characteristic parameters with the highestfitness value, wherein W_(i) represents an early warning degree of ani^(th) group of the combination of the characteristic parameters withthe highest fitness value, a_(i) represents a real-time value of a firstcharacteristic parameter in the i^(th) group of the combination of thecharacteristic parameters with the highest fitness value, b_(i)represents a real-time value of a second characteristic parameter in thei^(th) group of the combination of the characteristic parameters withthe highest fitness value, V₁ represents a first optimal critical valuecorresponding to the first characteristic parameter in the i^(th) groupof the combination of the characteristic parameters with the highestfitness value; and V₂ represent a second optimal critical valuecorresponding to the second characteristic parameter in the i^(th) groupof the combination of the characteristic parameters with the highestfitness value; in the earth sound monitoring system and the hydraulicsupport monitoring system, the single system early warning degree is anearly warning degree of each sensor, wherein, W_(i) represents an earlywarning degree of an i^(th) sensor, a_(i) represents a real-time valueof a characteristic parameters in the combination of the characteristicparameters with the highest fitness value corresponding to the i^(th)sensor, respectively, b_(i) represents a real-time values of a V₁represents a third optimal critical value corresponding to the thirdcharacteristic parameter in the combination of the characteristicparameters with the highest fitness value corresponding to the i^(th)sensor, V₂ represents a fourth characteristic parameter in thecombination of the characteristic parameters with the highest fitnessvalue corresponding to the i^(th) sensor; based on the single systemearly warning degree of each monitoring system, calculating acomprehensive early warning index W_(C) of each monitoring systemaccording to the following formula:$W_{C} = {\sum\left( {\frac{W_{i}}{\max\left( W_{i} \right)} \times \frac{R_{i}}{\sum R_{i}}} \right)}$wherein in the micro-seismic monitoring system, R_(i) represents afitness value of the i^(th) group of the combination of thecharacteristic parameters with the highest fitness value; in the earthsound monitoring system and the hydraulic support monitoring system,R_(i) represents a fitness value of the combination of thecharacteristic parameters with the highest fitness value correspondingto the i^(th) sensor; and based on the comprehensive early warning indexof each monitoring system, calculating an early warning effectivenessR_(I) of each monitoring system according to the following formula,wherein R_(I) represents an early warning effectiveness of an I^(th)monitoring system:$R_{I} = {\frac{n_{1}^{1}}{N_{1}} - \frac{t_{0}}{T_{0}}}$ wherein, n₁ ¹represents a number of times of early warnings generated and alarmedrightly in a monitoring time; N₁ represents a total number of times ofevents with large energy or shock in the monitoring time; t₀ is timetaken to generate an early warning; T₀ is total monitoring time; whenthe comprehensive early warning index corresponding to one of theplurality of monitoring systems exceeds a preset threshold within presetdays before an event with large energy or shock occurs, an early warmingis right, and when the comprehensive early warning index correspondingto the one of the plurality of monitoring systems does not exceed thepreset threshold within the preset days before the event with largeenergy or shock occurs, the early warming is false.
 9. The multi-system,multi-parameter, integrated, comprehensive early warning method for thecoal and rock dynamic disaster according to claim 8, wherein the step ofcalculating the multi-system comprehensive early warning result of thecoal and rock dynamic disaster based on the comprehensive early warningindex and the early warning effectiveness of each monitoring systemcomprises: based on the comprehensive early warning index and the earlywarning effectiveness of each monitoring system, calculating themulti-system comprehensive early warning result I_(C) of the coal androck dynamic disaster according to the following formula:$I_{C} = {\sum\left( {\frac{W_{CI}}{\max\left( W_{CI} \right)} \times \frac{R_{I}}{\sum R_{I}}} \right)}$wherein W_(CI) represents the comprehensive early warning index of theI^(th) monitoring system, and R_(I) represents the early warningeffectiveness of the I^(th) monitoring system.
 10. A multi-system,multi-parameter, integrated, comprehensive early warning system for acoal and rock dynamic disaster, comprising: a multivariatecharacteristic parameter extracting module, configured to obtainmonitoring data of a plurality of monitoring systems for the coal rockdynamic disaster, and extract multivariate characteristic parameters ineach monitoring system based on the monitoring data, wherein themultivariate characteristic parameters reflect precursor information ofthe coal and rock dynamic disaster; a characteristic parametercombination screening module, configured to screen out a combination ofcharacteristic parameters with a highest early warning effectiveness ineach monitoring system and an optimal critical value of eachcharacteristic parameter based on the multivariate characteristicparameters; a single system early warning index and early warningeffectiveness calculation module, configured to calculate acomprehensive early warning index and an early warning effectiveness ofeach monitoring system based on the combination of the characteristicparameters with the highest early warning effectiveness in eachmonitoring system and the optimal critical value of each characteristicparameter; and a comprehensive early warning result calculation module,configured to calculate a multi-system comprehensive early warningresult of the coal and rock dynamic disaster based on the comprehensiveearly warning index and the early warning effectiveness of eachmonitoring system.